Integrated Analysis of Ferroptosis- and Cellular Senescence-Related Biomarkers in Atherosclerosis Based on Machine Learning and Single-Cell Sequencing Data

作者：Xiang Qi, Shan Cao, Jian Chen, Xiaolei Yin · 发表于：Journal of Inflammation Research · 年份：2025 · DOI：10.2147/jir.s529581 · 被引用次数：3 · 研究领域：Ferroptosis and cancer prognosis、Single-cell and spatial transcriptomics、Cancer-related molecular mechanisms research

Background: Atherosclerosis is a chronic inflammatory disease characterized by lipid accumulation in the vascular wall. The roles of ferroptosis and cellular senescence in Atherosclerosis remain unclear. This study aimed to identify genes related to ferroptosis and cellular senescence in Atherosclerosis using bioinformatics approaches. Methods: Atherosclerosis gene expression datasets were obtained from the GEO database. Differentially expressed genes (DEGs) were identified and intersected with key genes from WGCNA modules, ferroptosis-related genes, and senescence-related genes to obtain common genes (CF-DEGs). Consensus clustering based on CF-DEGs was conducted to identify molecular subtypes, followed by differential expression analysis. Enrichment and immune infiltration analyses were used to investigate the biological functions and immune features of subtype-specific differentially expressed genes. Eight machine learning algorithms were applied to identify hub genes and construct a diagnostic model. Single-cell RNA-seq data were used to assess the roles of hub genes in cell communication and differentiation. Finally, animal experiments were performed to validate the expression of the hub genes. Results: A total of 23 CF-DEGs were identified, based on which two molecular subtypes were defined. A total of 421 DEGs were found between subtypes. Immune infiltration analysis revealed significant differences in eight immune cell types, including activated dendritic cells, macrop...